Impact of Microscopic Vehicle Mobility on Cluster-Based Routing Overhead in <roman>VANETs</roman>
Bibliographic record
Abstract
Node clustering is a potential solution to minimize the control signaling overhead of routing protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and frequent network topology changes induce instability to node clusters. Node cluster instability inflicts new challenges in maintaining a long route between network nodes, thus increasing the routing overhead. As a result, cluster instability, which is foisted by vehicle mobility, is a crucial issue for cluster-based routing in VANETs. This paper presents a stochastic analysis of the impact of cluster instability on generic routing overhead. A stochastic cluster instability model is adopted to capture the time variations of the cluster structure in terms of the cluster membership change rate and the cluster-overlap state change rate. First, we derive the probability distribution of the intracluster routing overhead using the cluster membership change rate. Second, the intercluster routing overhead is modeled as a rooted tree, with the tree nodes representing the value of the overhead and the tree edges weighted by the probability of a cluster-overlap state change. Numerical results are presented to evaluate the proposed models, which demonstrate a close agreement between analytical and simulation results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".